Range Resource Management and Scheduling Requirements and Guide

Deep learning (DL) is gaining popularity in various fields, such as computer vision, speech identification, and self-driving vehicles. Blackwell graphical processing units (GPUs) and Grace central processing units (CPUs) are widely utilized in computing platforms, including portable gadgets and supercomputers. Optimizing performance for multiple-programming workloads on such devices is crucial, as a single application cannot typically utilize all available resources. Developing effective GPU resource administration and scheduling systems is crucial for effective distribution among several clients. Nevertheless, current versions have several flaws. Clients often provide wrong task resource requirements, resulting in cluster resource low utilization. DL-based resource scheduling without considering cluster network features leads to poor operational efficiency. To address these difficulties, this study develops a distributed data center (DDC) framework using Blackwell GPUs and Grace CPUs for resource scheduling and accelerated computing with DL. This study offers a regression model-based technique for estimating task resource requirements, preventing clients from overallocating computer resources. This paper demonstrates smart cluster network-effective scheduling approaches for both prompt and batch forms based on the resource need assessment methodologies described. Study findings demonstrate that the suggested system can improve task computation by 19% overall and reduce mean job completion duration (JCD) by 22% when evaluated against modern approaches. Furthermore, the presented optimization approaches are useful in a variety of contexts.

Currently, the scheduling mode used by most TT&C resource scheduling system was based on the mission requirements offered by users. After the scheduling plan is formulated, the resource redundancy has been basically determined, without the advance planning of the resources. This article analyzed the data of the scheduling system, by dividing the requirements of resources into two types of “period” and “peak”. Through analyzing the periodic use of resource historical data, the periodic resource demand will be predicted and guided resources for time-dividing and domain-planning. By analyzing the allocation data of dynamic resource scheduling, the peak resource will be reserved to enable and recycle dynamically in actual. Finally the strategy was verified by simulation, which provided a different approach to improve the utilization rate of the entire network resources.KeywordsData miningPeriodic resourcePeak resource

The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.

In order to resolve resource management and scheduling problem in Manufacturing Grid (MG) – an application of Grid technology, we develop a resource management and scheduling system with the interaction of Manufacturing Grid Information Service (MGIS) and the Manufacturing Grid Resource Scheduler (MGRS). The former, MGIS, provides fundamental mechanisms for remote resource encapsulating, registration, and monitoring, and the latter, MGRS, performs scheduling roles as Global Process Planning (GPP) analyzing, resource discovery, resource selection, and resource mapping.

Performance evaluation of grid resource management and scheduling system is of key importance to the success of the grid. In this paper, we introduce a generalized stochastic Petri net (GSPN) model for such purpose. The model is simple with clarity and can be applied to other research issues on grid to provide them with performance evaluation capabilities.

Edge computing is a domain that processes extensive data by integrating data processing and storage at one device. The vast improvement in resource deployment for edge environments is the primary reason for the difficulties in resource scheduling and security management. In this paper, a Novel Edge Computing Resource Scheduling (NECRS) system, such as the Lightweight Security-Aware Resource Scheduler (LSARS), is presented. The proposed LSARS logically distributes computing resources and assesses the security risk, based on the trust measure, data sensitivity, and node vulnerability. The proposed NECRS utilizes a hybrid decision-making approach that integrates a heuristic-based scheduling technique with lightweight anomaly detection to provide low latency and resilient task execution times. Extensive experimental results demonstrate that LSARS outperforms conventional scheduling schemes in terms of resource utilization, scheduling efficiency, and attack mitigation. Finally, the proposed approach improved the security and responsiveness of edge-enabled services in real-world deployments.

Efficient scheduling is crucial for time and cost-effective utilization of compute resources especially for high end systems. A variety of factors need to be considered during the scheduling decisions. Power variation across the compute resources of homogeneous large-scale systems has not been considered so far. This paper discusses the impact of the power variation for parallel application scheduling. It addresses the problem of finding the optimal resource configuration for a given application that will minimize the amount of consumed energy, under pre-defined constraints on application execution time and instantaneous average power consumption. This paper presents an efficient algorithm to do so, which also considers the existing power diversity among the compute nodes (modified also at different operating CPU frequencies) of a given homogeneous High Performance Computing system. Based on this algorithm, the paper presents a plug-in, referred to as Configuration Adviser, which operates on top of a given resource management and scheduling system to advise on energy-wise optimal resource configuration for a given application, execution using which, will adhere to the specified execution time and power consumption constraints. The main goal of this plug-in is to enhance the current resource management and scheduling tools for the support of power capping for future Exascale systems, where a data center might not be able to provide cooling or electrical power for system peak consumption but only for the expected power bands.

The planning and deployment of mega constellations has posed increasing pressure on the existing telemetry, track and command (TTC) resource scheduling strategy. The current plan-based scheduling strategy distributes resources according to needs, however, it cannot address sudden resource requests with prompt responses. By analyzing the status quo of resource scheduling systems, we have proposed a distributed collaborative scheduling technology for random access in real-time, based on the theory of low costs and rapid transfers of scheduling task resources. First, we use knowledge graph technology to map the link between scheduling tasks and idle resources and develop a graph network. Furthermore, by analyzing and evaluating the resource transfer capability of a multi-target TTC system in hemispherical coverage (MT-TTC), we propose the concept of a virtual resource pool and develop a two-layer scheduling framework in which the central scheduling system coordinates with multiple regional systems. This scheduling strategy works well when a sudden TTC request occurs. The central system manages the satellite requests and make plans, while regional systems are responsible for handling the sudden requests and report to the central system on its resource change. With regional systems sharing some degree of autonomy, the central system making a final decision and systems across the framework coordinating in real-time, we design a model on resource transfers for MT-TTC equipment and a collaboration mechanism that coordinates the resource transfers across MT-TTC equipment and other single-beam equipment within and between regions. We do experiments in simulation scheduling environments. The results show that the regional scheduling system based on MT-TTC equipment is able to manage continuous random-access requests in real-time, and avoid resource conflicts across regions by collaborating with the central scheduling system. It is of value to be applied in real scenarios.

The modern large multi-channel broadcast facility is far more then a multiplicity of traditional single-channel television stations. Business requirements for large DBS or digital cable services demand that such a system manage hundreds of play-out channels, sharing content across output streams, with extraordinary reliability and maximized operational efficiency. Achieving these goals requires an entirely new approach to resource management, automation and scheduling systems. This paper describes a new architecture for the Multichannel Broadcast System, using a shared object-oriented database to support broadcast workflow starting from the acquisition of program content through on-air operation. Automated systems support the tracking of channel personalities, program content, physical media and broadcast resources such as video file server ports, all on a timeline basis. Communication with the many external subsystems required in a DVB environment is fully integrated into the system architecture: Electronic Program Guides, Conditional Access Systems, Transmission Bandwidth control and machine automation are all choreographed for maximum efficiency. This system has now been implemented at two sites worldwide. Examples from the most sophisticated project - the 320 channel ASkyB Broadcast Center in Gilbert Arizona - will be used to illustrate a typical deployment of the System.

Distributed computing resources are aggregated in a high-performance computing environment. The resources are appropriately allocated according to the user assignments. However, current resources scheduling strategies overly focus on system performance rather than the user fairness. Moreover, most strategies lack the environment adaptability. In order to solve these problems, this paper selects five different priority variables and proposes a multi-dimensional intelligent heterogeneous resource scheduling method for a high-performance cluster. It introduces the job environment queue, refines the state of the job system layer, optimizes the job priority scheduling strategy, and uses the genetic algorithm to optimize the priority parameters to increase the configurability of the job scheduling strategy. Using the algorithm, the overall performance of the platform is enhanced by 12.3% based on the PBS (Portable Batch System) scheduling system.

Resource scheduling is one of the main problems for multi-satellite Tracking, Telemetry and Command (TT&C) networks. Traditional multi-resource joint scheduling algorithms are with long solution time, low efficiency, high computational cost, and simple description on the system. Deep Neural Network (DNN) provides a possible new way to solve those problems, but it is difficult to handle correlations among the input data. This motivates our work to solve the strong correlation problem based on the accumulated historical data, and thus enables DNN for TT&C resource scheduling. By discretizing the data, multiple constraints and related attributes are transformed into different flags, and some binary bits of the data are used to reflect the constraint relationship. Then, we can use DNN model and construct an intelligent TT&C resource scheduling system to handle multiple constraints and data attributes (such as priorities among tasks and others). This improves the efficiency of TT&C resources utilization and automation. Effectiveness of the proposed model is verified by simulations.

Clusters, Grids, and peer‐to‐peer (P2P) networks have emerged as popular paradigms for next generation parallel and distributed computing. They enable aggregation of distributed resources for solving large‐scale problems in science, engineering, and commerce. In Grid and P2P computing environments, the resources are usually geographically distributed in multiple administrative domains, managed and owned by different organizations with different policies, and interconnected by wide‐area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, continuously changing resource conditions, and politics. The resource management and scheduling systems for Grid computing need to manage resources and application execution depending on either resource consumers' or owners' requirements, and continuously adapt to changes in resource availability.The management of resources and scheduling of applications in such large‐scale distributed systems is a complex undertaking. In order to prove the effectiveness of resource brokers and associated scheduling algorithms, their performance needs to be evaluated under different scenarios such as varying number of resources and users with different requirements. In a Grid environment, it is hard and even impossible to perform scheduler performance evaluation in a repeatable and controllable manner as resources and users are distributed across multiple organizations with their own policies. To overcome this limitation, we have developed a Java‐based discrete‐event Grid simulation toolkit called GridSim. The toolkit supports modeling and simulation of heterogeneous Grid resources (both time‐ and space‐shared), users and application models. It provides primitives for creation of application tasks, mapping of tasks to resources, and their management. To demonstrate suitability of the GridSim toolkit, we have simulated a Nimrod‐G like Grid resource broker and evaluated the performance of deadline and budget constrained cost‐ and time‐minimization scheduling algorithms. Copyright © 2002 John Wiley & Sons, Ltd.

An intelligent system that schedules Space Shuttle activities

In view of the traditional method to evaluate the impact of MOOC model on traditional education and teaching, there is a problem of poor adaptive performance and low resource scheduling ability. A teaching ability evaluation combination model based on resource scheduling data envelopment analysis method is constructed. This method uses the traditional teaching mode to fuzzy clustering MOOC subject resources, analyses the correlation function of teaching ability, and uses the association rule mining method to mine the teaching ability characteristics. According to the teaching ability and the linear combination model of traditional education and teaching, the MOOC learning resources are completed. Data scheduling; construct a statistical model of data envelopment analysis to quantitatively assess the impact of MOOC model on teaching ability and traditional education and teaching. The experimental results show that the proposed method has good adaptive performance, high resource scheduling capability and high accuracy.

As Grid technologies evolve quickly on Internet, research related to resource scheduling faces new opportunities and challenges. These new technologies, ideas and approaches provide a new environment for researching and developing the economy-based resource scheduling system. Aiming at the hierarchical Grid model, following utility optimization thinking, while an universal flexible utility function is provided for addressing the Grid users’ QoS requirements of deadline and budget, this paper proposes a new Grid resource scheduling algorithm, which can not only increase the utilization of resources and system throughput, but also realize cost-time optimization between resource providers and users within Grid systems. On the basis of having described the different functional parts of the proposed algorithm, two kinds of simulative experiments about completion time and cost of tasks are conducted. Finally, the experiment results show that the proposed algorithm is effective.